Overview

Dataset statistics

Number of variables16
Number of observations315
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory39.5 KiB
Average record size in memory128.4 B

Variable types

Categorical1
TimeSeries11
Numeric4

Alerts

Period has a high cardinality: 315 distinct valuesHigh cardinality
Gross Domestic Product, Year On Year Growth Rate | GDP In Chained (2015) Dollars is highly overall correlated with Merchandise Imports By Commodity Division | Total Merchandise Imports and 6 other fieldsHigh correlation
Sea Cargo And Shipping Statistics (Total Cargo) | Total Container Throughput (Thousand Twenty-Foot Equivalent Units) is highly overall correlated with Composite Leading Index (2015 = 100) | Quarterly Composite Leading IndexHigh correlation
Merchandise Imports By Commodity Division | Total Merchandise Imports is highly overall correlated with Gross Domestic Product, Year On Year Growth Rate | GDP In Chained (2015) Dollars and 5 other fieldsHigh correlation
Index Of Industrial Production (2019 = 100) | Total is highly overall correlated with Gross Domestic Product, Year On Year Growth Rate | GDP In Chained (2015) Dollars and 4 other fieldsHigh correlation
Domestic Exports Of Major Non-Oil Products | Total Electronic Products is highly overall correlated with Merchandise Imports By Commodity Division | Total Merchandise Imports and 1 other fieldsHigh correlation
Composite Leading Index (2015 = 100) | Quarterly Composite Leading Index is highly overall correlated with Gross Domestic Product, Year On Year Growth Rate | GDP In Chained (2015) Dollars and 5 other fieldsHigh correlation
Business Expectations For The Services Sector - General Business Outlook For The Next 6 Months, Weighted Percentages Of Up, Same, Down | Net Weighted Balance - Total Services Sector is highly overall correlated with Gross Domestic Product, Year On Year Growth Rate | GDP In Chained (2015) Dollars and 6 other fieldsHigh correlation
Foreign Wholesale Trade Index, (2017 = 100), In Chained Volume Terms | Total is highly overall correlated with Gross Domestic Product, Year On Year Growth Rate | GDP In Chained (2015) Dollars and 2 other fieldsHigh correlation
Job Vacancies By Industry And Occupational Group (SSIC 2020) (End Of Period) | Total is highly overall correlated with Gross Domestic Product, Year On Year Growth Rate | GDP In Chained (2015) Dollars and 4 other fieldsHigh correlation
Unemployment Rate (End Of Period) | Total Unemployment Rate is highly overall correlated with Gross Domestic Product, Year On Year Growth Rate | GDP In Chained (2015) Dollars and 3 other fieldsHigh correlation
Food & Beverage Services Index, (2017 = 100), In Chained Volume Terms | Total is non stationaryNon stationary
Period is uniformly distributedUniform
Period has unique valuesUnique
Food & Beverage Services Index, (2017 = 100), In Chained Volume Terms | Total has unique valuesUnique
Retail Sales Index, (2017 = 100), In Chained Volume Terms | Total has unique valuesUnique
Sea Cargo And Shipping Statistics (Total Cargo) | Total Container Throughput (Thousand Twenty-Foot Equivalent Units) has unique valuesUnique
Merchandise Imports By Commodity Division | Total Merchandise Imports has unique valuesUnique
Domestic Exports Of Major Non-Oil Products | Total Electronic Products has unique valuesUnique
Domestic Wholesale Trade Index, (2017 = 100), In Chained Volume Terms | Total has unique valuesUnique
Foreign Wholesale Trade Index, (2017 = 100), In Chained Volume Terms | Total has unique valuesUnique
Job Vacancies By Industry And Occupational Group (SSIC 2020) (End Of Period) | Total has unique valuesUnique
Unemployment Rate (End Of Period) | Total Unemployment Rate has 17 (5.4%) zerosZeros

Reproduction

Analysis started2023-06-17 02:07:05.775629
Analysis finished2023-06-17 02:07:17.757918
Duration11.98 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Period
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct315
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
1997-01-31
 
1
2014-04-30
 
1
2014-11-30
 
1
2014-10-31
 
1
2014-09-30
 
1
Other values (310)
310 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3150
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique315 ?
Unique (%)100.0%

Sample

1st row1997-01-31
2nd row1997-02-28
3rd row1997-03-31
4th row1997-04-30
5th row1997-05-31

Common Values

ValueCountFrequency (%)
1997-01-31 1
 
0.3%
2014-04-30 1
 
0.3%
2014-11-30 1
 
0.3%
2014-10-31 1
 
0.3%
2014-09-30 1
 
0.3%
2014-08-31 1
 
0.3%
2014-07-31 1
 
0.3%
2014-06-30 1
 
0.3%
2014-05-31 1
 
0.3%
2014-03-31 1
 
0.3%
Other values (305) 305
96.8%

Length

2023-06-17T10:07:17.784947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1997-01-31 1
 
0.3%
1997-10-31 1
 
0.3%
1997-04-30 1
 
0.3%
1997-05-31 1
 
0.3%
1997-06-30 1
 
0.3%
1997-07-31 1
 
0.3%
1997-08-31 1
 
0.3%
1997-09-30 1
 
0.3%
2000-02-29 1
 
0.3%
1997-12-31 1
 
0.3%
Other values (305) 305
96.8%

Most occurring characters

ValueCountFrequency (%)
0 802
25.5%
- 630
20.0%
1 507
16.1%
2 434
13.8%
3 342
10.9%
9 140
 
4.4%
8 83
 
2.6%
7 62
 
2.0%
4 50
 
1.6%
6 50
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2520
80.0%
Dash Punctuation 630
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 802
31.8%
1 507
20.1%
2 434
17.2%
3 342
13.6%
9 140
 
5.6%
8 83
 
3.3%
7 62
 
2.5%
4 50
 
2.0%
6 50
 
2.0%
5 50
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 630
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3150
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 802
25.5%
- 630
20.0%
1 507
16.1%
2 434
13.8%
3 342
10.9%
9 140
 
4.4%
8 83
 
2.6%
7 62
 
2.0%
4 50
 
1.6%
6 50
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 802
25.5%
- 630
20.0%
1 507
16.1%
2 434
13.8%
3 342
10.9%
9 140
 
4.4%
8 83
 
2.6%
7 62
 
2.0%
4 50
 
1.6%
6 50
 
1.6%
Distinct225
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7857143
Minimum-11.9
Maximum18.6
Zeros0
Zeros (%)0.0%
Memory size2.6 KiB
2023-06-17T10:07:17.819148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-11.9
5-th percentile-3.69
Q12.6833333
median4.7666667
Q38.0333333
95-th percentile11.636667
Maximum18.6
Range30.5
Interquartile range (IQR)5.35

Descriptive statistics

Standard deviation4.6261079
Coefficient of variation (CV)0.96664942
Kurtosis0.99326425
Mean4.7857143
Median Absolute Deviation (MAD)2.7666667
Skewness-0.26912577
Sum1507.5
Variance21.400875
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.0002817928356
2023-06-17T10:07:17.859692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.8 6
 
1.9%
1.6 4
 
1.3%
3.3 3
 
1.0%
4.766666667 3
 
1.0%
6.2 3
 
1.0%
4.5 3
 
1.0%
4 3
 
1.0%
5.733333333 3
 
1.0%
5.3 3
 
1.0%
4.7 3
 
1.0%
Other values (215) 281
89.2%
ValueCountFrequency (%)
-11.9 1
0.3%
-9.366666667 1
0.3%
-7.7 1
0.3%
-7.5 1
0.3%
-6.833333333 1
0.3%
-6.266666667 1
0.3%
-5.533333333 1
0.3%
-5 1
0.3%
-4.9 1
0.3%
-4.833333333 2
0.6%
ValueCountFrequency (%)
18.6 1
0.3%
17.83333333 1
0.3%
17.3 1
0.3%
17.06666667 1
0.3%
16.3 1
0.3%
15.83333333 1
0.3%
14.43333333 1
0.3%
13.5 1
0.3%
13.3 1
0.3%
13.13333333 1
0.3%
2023-06-17T10:07:17.988892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF
Distinct315
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.06062
Minimum46.492
Maximum126.343
Zeros0
Zeros (%)0.0%
Memory size2.6 KiB
2023-06-17T10:07:18.207790image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum46.492
5-th percentile78.7206
Q197.9375
median102.316
Q3107.3495
95-th percentile114.0092
Maximum126.343
Range79.851
Interquartile range (IQR)9.412

Descriptive statistics

Standard deviation10.629739
Coefficient of variation (CV)0.10518181
Kurtosis5.5085986
Mean101.06062
Median Absolute Deviation (MAD)4.617
Skewness-1.7665348
Sum31834.095
Variance112.99135
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.1198420854
2023-06-17T10:07:18.285332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111.147 1
 
0.3%
103.435 1
 
0.3%
106.715 1
 
0.3%
110.426 1
 
0.3%
106.164 1
 
0.3%
112.559 1
 
0.3%
106.511 1
 
0.3%
106.363 1
 
0.3%
110.278 1
 
0.3%
108.211 1
 
0.3%
Other values (305) 305
96.8%
ValueCountFrequency (%)
46.492 1
0.3%
48.371 1
0.3%
56.624 1
0.3%
60.51 1
0.3%
69.942 1
0.3%
70.065 1
0.3%
71.238 1
0.3%
71.496 1
0.3%
73.172 1
0.3%
74.419 1
0.3%
ValueCountFrequency (%)
126.343 1
0.3%
125.698 1
0.3%
118.447 1
0.3%
117.528 1
0.3%
117.231 1
0.3%
116.93 1
0.3%
116.72 1
0.3%
116.555 1
0.3%
116.202 1
0.3%
116.091 1
0.3%
2023-06-17T10:07:18.540519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF
Distinct315
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3883725
Minimum-52.31033
Maximum81.949136
Zeros0
Zeros (%)0.0%
Negative122
Negative (%)38.7%
Memory size2.6 KiB
2023-06-17T10:07:18.689502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-52.31033
5-th percentile-10.548569
Q1-2.9020329
median1.9792657
Q38.3024861
95-th percentile26.467143
Maximum81.949136
Range134.25947
Interquartile range (IQR)11.204519

Descriptive statistics

Standard deviation12.10186
Coefficient of variation (CV)3.5715848
Kurtosis8.5489524
Mean3.3883725
Median Absolute Deviation (MAD)5.5844859
Skewness1.2031175
Sum1067.3373
Variance146.45501
MonotonicityNot monotonic
2023-06-17T10:07:18.746904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.71509756 1
 
0.3%
-6.235164835 1
 
0.3%
4.006201135 1
 
0.3%
6.333720131 1
 
0.3%
1.680357629 1
 
0.3%
0.8699988917 1
 
0.3%
0.4714689515 1
 
0.3%
-2.822257592 1
 
0.3%
-5.102432845 1
 
0.3%
0.3573057625 1
 
0.3%
Other values (305) 305
96.8%
ValueCountFrequency (%)
-52.31033049 1
0.3%
-41.21943196 1
0.3%
-28.26402481 1
0.3%
-19.71147008 1
0.3%
-19.49591976 1
0.3%
-15.1068278 1
0.3%
-15.01602097 1
0.3%
-14.92432423 1
0.3%
-14.36988927 1
0.3%
-14.32945499 1
0.3%
ValueCountFrequency (%)
81.94913592 1
0.3%
56.72549532 1
0.3%
46.12575421 1
0.3%
43.20228763 1
0.3%
36.74623577 1
0.3%
32.23641315 1
0.3%
32.01258168 1
0.3%
29.77863851 1
0.3%
28.79486406 1
0.3%
28.73795495 1
0.3%
Distinct315
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4343284
Minimum-20.288644
Maximum22.884381
Zeros0
Zeros (%)0.0%
Memory size2.6 KiB
2023-06-17T10:07:18.826168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-20.288644
5-th percentile-13.088102
Q10.89660289
median5.687237
Q39.3957885
95-th percentile15.167363
Maximum22.884381
Range43.173025
Interquartile range (IQR)8.4991856

Descriptive statistics

Standard deviation8.0982017
Coefficient of variation (CV)1.8262521
Kurtosis0.79080344
Mean4.4343284
Median Absolute Deviation (MAD)4.2642579
Skewness-0.85184777
Sum1396.8135
Variance65.580871
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.006262744925
2023-06-17T10:07:18.867905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.488212275 1
 
0.3%
6.225740163 1
 
0.3%
6.090635787 1
 
0.3%
5.223272779 1
 
0.3%
3.457290133 1
 
0.3%
1.813214108 1
 
0.3%
-0.2506265664 1
 
0.3%
4.120798474 1
 
0.3%
4.107408194 1
 
0.3%
7.247418391 1
 
0.3%
Other values (305) 305
96.8%
ValueCountFrequency (%)
-20.28864413 1
0.3%
-19.83625022 1
0.3%
-19.59602541 1
0.3%
-18.63251621 1
0.3%
-17.71832209 1
0.3%
-17.01622477 1
0.3%
-16.41359364 1
0.3%
-16.05430997 1
0.3%
-15.96553813 1
0.3%
-15.88301147 1
0.3%
ValueCountFrequency (%)
22.88438099 1
0.3%
20.90217676 1
0.3%
20.33070061 1
0.3%
20.17464424 1
0.3%
19.85554911 1
0.3%
18.20100237 1
0.3%
18.12702593 1
0.3%
17.97892461 1
0.3%
17.57780226 1
0.3%
16.7383838 1
0.3%
2023-06-17T10:07:19.143265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF
Distinct308
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92392.333
Minimum61659
Maximum122889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-06-17T10:07:19.304308image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum61659
5-th percentile77653.6
Q187469.5
median92348
Q397474
95-th percentile108548.6
Maximum122889
Range61230
Interquartile range (IQR)10004.5

Descriptive statistics

Standard deviation9426.5344
Coefficient of variation (CV)0.10202724
Kurtosis0.53076938
Mean92392.333
Median Absolute Deviation (MAD)4882
Skewness-0.00070736847
Sum29103585
Variance88859550
MonotonicityNot monotonic
2023-06-17T10:07:19.404129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80564 5
 
1.6%
93726 2
 
0.6%
101294 2
 
0.6%
90711.4 2
 
0.6%
93757 1
 
0.3%
95041 1
 
0.3%
92336 1
 
0.3%
96100 1
 
0.3%
89190 1
 
0.3%
93653 1
 
0.3%
Other values (298) 298
94.6%
ValueCountFrequency (%)
61659 1
0.3%
62847 1
0.3%
67849 1
0.3%
70340 1
0.3%
70843 1
0.3%
71397 1
0.3%
71764 1
0.3%
74679 1
0.3%
74766 1
0.3%
75425 1
0.3%
ValueCountFrequency (%)
122889 1
0.3%
120489 1
0.3%
117170 1
0.3%
112168 1
0.3%
111866 1
0.3%
111324 1
0.3%
110311 1
0.3%
109866 1
0.3%
109823 1
0.3%
109709 1
0.3%

Merchandise Imports By Commodity Division | Total Merchandise Imports
Numeric time series

HIGH CORRELATION  UNIQUE 

Distinct315
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.930571
Minimum-33.491589
Maximum39.889863
Zeros0
Zeros (%)0.0%
Memory size2.6 KiB
2023-06-17T10:07:19.450583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-33.491589
5-th percentile-20.073903
Q1-4.9836138
median6.7292428
Q317.141169
95-th percentile29.672523
Maximum39.889863
Range73.381452
Interquartile range (IQR)22.124783

Descriptive statistics

Standard deviation15.006435
Coefficient of variation (CV)2.5303525
Kurtosis-0.5209166
Mean5.930571
Median Absolute Deviation (MAD)11.224699
Skewness-0.20351801
Sum1868.1299
Variance225.19309
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.001595319918
2023-06-17T10:07:19.495255image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.600448256 1
 
0.3%
1.761717344 1
 
0.3%
-8.258369267 1
 
0.3%
-8.270330523 1
 
0.3%
-2.506644734 1
 
0.3%
-8.874223212 1
 
0.3%
-6.499156527 1
 
0.3%
-2.609732788 1
 
0.3%
-0.3685846947 1
 
0.3%
15.0303535 1
 
0.3%
Other values (305) 305
96.8%
ValueCountFrequency (%)
-33.4915889 1
0.3%
-31.28406546 1
0.3%
-28.2494616 1
0.3%
-27.96860629 1
0.3%
-26.969079 1
0.3%
-26.77141374 1
0.3%
-26.21945538 1
0.3%
-26.02199724 1
0.3%
-24.83285081 1
0.3%
-22.90513061 1
0.3%
ValueCountFrequency (%)
39.88986327 1
0.3%
38.60607068 1
0.3%
35.35043227 1
0.3%
34.91079086 1
0.3%
34.36428054 1
0.3%
32.69615912 1
0.3%
32.43642823 1
0.3%
32.3623983 1
0.3%
32.11069546 1
0.3%
31.76317177 1
0.3%
2023-06-17T10:07:19.555805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF

M1 Money Supply
Real number (ℝ)

Distinct304
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.900459
Minimum-11.782682
Maximum248.97563
Zeros0
Zeros (%)0.0%
Negative29
Negative (%)9.2%
Memory size2.6 KiB
2023-06-17T10:07:19.683260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-11.782682
5-th percentile-0.97579643
Q14.1794074
median9.3165997
Q315.459628
95-th percentile23.934417
Maximum248.97563
Range260.75831
Interquartile range (IQR)11.280221

Descriptive statistics

Standard deviation15.828416
Coefficient of variation (CV)1.4520872
Kurtosis163.33646
Mean10.900459
Median Absolute Deviation (MAD)5.6523411
Skewness10.898659
Sum3433.6445
Variance250.53876
MonotonicityNot monotonic
2023-06-17T10:07:19.793239image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.87344154 12
 
3.8%
9.938032592 1
 
0.3%
6.900927518 1
 
0.3%
7.819063936 1
 
0.3%
10.7348966 1
 
0.3%
9.870150545 1
 
0.3%
11.91633856 1
 
0.3%
14.59850429 1
 
0.3%
16.53153085 1
 
0.3%
16.94660488 1
 
0.3%
Other values (294) 294
93.3%
ValueCountFrequency (%)
-11.78268167 1
0.3%
-11.03852525 1
0.3%
-10.28868034 1
0.3%
-9.794338218 1
0.3%
-8.443790243 1
0.3%
-7.708125893 1
0.3%
-6.520081923 1
0.3%
-5.771421203 1
0.3%
-4.773367778 1
0.3%
-4.515273204 1
0.3%
ValueCountFrequency (%)
248.9756309 1
0.3%
33.09802867 1
0.3%
32.81745344 1
0.3%
31.78391275 1
0.3%
31.23188308 1
0.3%
29.69961704 1
0.3%
28.9947376 1
0.3%
28.38085819 1
0.3%
28.13359568 1
0.3%
26.94176174 1
0.3%
Distinct314
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9179108
Minimum-32.247239
Maximum58.639049
Zeros2
Zeros (%)0.6%
Negative95
Negative (%)30.2%
Memory size2.6 KiB
2023-06-17T10:07:19.848051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-32.247239
5-th percentile-12.047338
Q1-2.3541537
median5.1460685
Q313.502651
95-th percentile25.318454
Maximum58.639049
Range90.886288
Interquartile range (IQR)15.856805

Descriptive statistics

Standard deviation12.288866
Coefficient of variation (CV)2.0765548
Kurtosis1.7438017
Mean5.9179108
Median Absolute Deviation (MAD)7.8615232
Skewness0.52342471
Sum1864.1419
Variance151.01624
MonotonicityNot monotonic
2023-06-17T10:07:19.890872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
0.6%
-5.407013057 1
 
0.3%
-1.926697851 1
 
0.3%
0.3202970872 1
 
0.3%
-1.158422041 1
 
0.3%
3.824068418 1
 
0.3%
2.644861 1
 
0.3%
0.7357242497 1
 
0.3%
-1.914109857 1
 
0.3%
5.314577826 1
 
0.3%
Other values (304) 304
96.5%
ValueCountFrequency (%)
-32.24723852 1
0.3%
-25.72058014 1
0.3%
-22.22087965 1
0.3%
-21.92427192 1
0.3%
-21.89996138 1
0.3%
-21.19065401 1
0.3%
-20.63702315 1
0.3%
-16.19398937 1
0.3%
-15.50422467 1
0.3%
-14.9308187 1
0.3%
ValueCountFrequency (%)
58.63904919 1
0.3%
52.40453996 1
0.3%
49.081461 1
0.3%
41.21096903 1
0.3%
41.04259351 1
0.3%
35.57267332 1
0.3%
32.21275218 1
0.3%
29.58174403 1
0.3%
28.48754502 1
0.3%
28.38423526 1
0.3%

Domestic Exports Of Major Non-Oil Products | Total Electronic Products
Numeric time series

HIGH CORRELATION  UNIQUE 

Distinct315
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.60038955
Minimum-39.19719
Maximum42.737464
Zeros0
Zeros (%)0.0%
Memory size2.6 KiB
2023-06-17T10:07:19.926879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-39.19719
5-th percentile-24.885023
Q1-10.307252
median-1.7371961
Q39.0355817
95-th percentile25.117754
Maximum42.737464
Range81.934654
Interquartile range (IQR)19.342833

Descriptive statistics

Standard deviation14.804003
Coefficient of variation (CV)-24.657329
Kurtosis-0.020780412
Mean-0.60038955
Median Absolute Deviation (MAD)9.7385044
Skewness0.1472854
Sum-189.12271
Variance219.15849
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.00356758835
2023-06-17T10:07:19.969097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.6365978323 1
 
0.3%
-8.730978284 1
 
0.3%
-10.24390816 1
 
0.3%
-3.649909512 1
 
0.3%
-4.032923472 1
 
0.3%
-6.871926916 1
 
0.3%
-7.92812208 1
 
0.3%
-17.39005352 1
 
0.3%
-15.31443954 1
 
0.3%
-16.04106242 1
 
0.3%
Other values (305) 305
96.8%
ValueCountFrequency (%)
-39.19718958 1
0.3%
-38.26542362 1
0.3%
-37.15245138 1
0.3%
-32.95197263 1
0.3%
-32.01821704 1
0.3%
-31.73208223 1
0.3%
-31.30941754 1
0.3%
-28.93354422 1
0.3%
-28.91219222 1
0.3%
-27.59796762 1
0.3%
ValueCountFrequency (%)
42.73746418 1
0.3%
40.22596246 1
0.3%
33.60964853 1
0.3%
33.07980819 1
0.3%
32.03439608 1
0.3%
31.65247882 1
0.3%
31.57466464 1
0.3%
30.86152577 1
0.3%
30.21828437 1
0.3%
29.21030872 1
0.3%
2023-06-17T10:07:20.115923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF
Distinct314
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1354587
Minimum-11.732456
Maximum21.788413
Zeros2
Zeros (%)0.6%
Memory size2.6 KiB
2023-06-17T10:07:20.222588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-11.732456
5-th percentile-8.3295354
Q1-1.6934258
median2.0105792
Q35.1631672
95-th percentile14.07061
Maximum21.788413
Range33.520869
Interquartile range (IQR)6.856593

Descriptive statistics

Standard deviation6.0350493
Coefficient of variation (CV)2.8261137
Kurtosis0.58002875
Mean2.1354587
Median Absolute Deviation (MAD)3.5632681
Skewness0.37963812
Sum672.6695
Variance36.42182
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.647959037 × 10-7
2023-06-17T10:07:20.347059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
0.6%
2.039052484 1
 
0.3%
3.061627471 1
 
0.3%
2.428229633 1
 
0.3%
2.366419426 1
 
0.3%
2.304609218 1
 
0.3%
2.577023803 1
 
0.3%
2.849438387 1
 
0.3%
3.121852971 1
 
0.3%
3.001401971 1
 
0.3%
Other values (304) 304
96.5%
ValueCountFrequency (%)
-11.73245614 1
0.3%
-11.51704616 1
0.3%
-11.30163618 1
0.3%
-11.0862262 1
0.3%
-10.82191781 1
0.3%
-10.62525307 1
0.3%
-10.42858834 1
0.3%
-10.2319236 1
0.3%
-9.343065693 1
0.3%
-9.224910519 1
0.3%
ValueCountFrequency (%)
21.7884131 1
0.3%
20.11567084 1
0.3%
18.51621906 1
0.3%
18.44292859 1
0.3%
16.77018634 1
0.3%
16.49484536 1
0.3%
16.37239165 1
0.3%
16.24408137 1
0.3%
16.11577109 1
0.3%
15.98746082 1
0.3%
2023-06-17T10:07:20.641258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF
Distinct168
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.247619
Minimum-58
Maximum36
Zeros1
Zeros (%)0.3%
Memory size2.6 KiB
2023-06-17T10:07:20.899295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-58
5-th percentile-31.2
Q1-3.8333333
median5.3333333
Q318.333333
95-th percentile28
Maximum36
Range94
Interquartile range (IQR)22.166667

Descriptive statistics

Standard deviation18.393418
Coefficient of variation (CV)4.330289
Kurtosis0.58415078
Mean4.247619
Median Absolute Deviation (MAD)12.333333
Skewness-0.87341215
Sum1338
Variance338.31782
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.72627735 × 10-5
2023-06-17T10:07:20.943916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 11
 
3.5%
11 8
 
2.5%
3 8
 
2.5%
18 7
 
2.2%
5 6
 
1.9%
26 6
 
1.9%
7 6
 
1.9%
22 5
 
1.6%
31 5
 
1.6%
9 5
 
1.6%
Other values (158) 248
78.7%
ValueCountFrequency (%)
-58 1
0.3%
-53 1
0.3%
-51.33333333 1
0.3%
-49.66666667 1
0.3%
-49 1
0.3%
-48 1
0.3%
-41 1
0.3%
-40.33333333 1
0.3%
-40 1
0.3%
-39.33333333 1
0.3%
ValueCountFrequency (%)
36 1
 
0.3%
35 1
 
0.3%
34 1
 
0.3%
33 1
 
0.3%
32 1
 
0.3%
31 5
1.6%
30.66666667 1
 
0.3%
30.33333333 1
 
0.3%
30 1
 
0.3%
29 1
 
0.3%
2023-06-17T10:07:21.170873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF
Distinct315
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8945303
Minimum-22.731706
Maximum21.386802
Zeros0
Zeros (%)0.0%
Memory size2.6 KiB
2023-06-17T10:07:21.280005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-22.731706
5-th percentile-13.377089
Q1-3.1853154
median0.60149943
Q35.8278189
95-th percentile12.485714
Maximum21.386802
Range44.118508
Interquartile range (IQR)9.0131343

Descriptive statistics

Standard deviation7.4345967
Coefficient of variation (CV)8.3111736
Kurtosis0.58904582
Mean0.8945303
Median Absolute Deviation (MAD)4.532719
Skewness-0.3490868
Sum281.77705
Variance55.273228
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.00516132621
2023-06-17T10:07:21.379071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4.953252948 1
 
0.3%
-0.7743446423 1
 
0.3%
4.596229575 1
 
0.3%
3.081620818 1
 
0.3%
1.56701206 1
 
0.3%
-0.4057826258 1
 
0.3%
-2.378577312 1
 
0.3%
-4.351371998 1
 
0.3%
-2.56285832 1
 
0.3%
1.014169036 1
 
0.3%
Other values (305) 305
96.8%
ValueCountFrequency (%)
-22.73170637 1
0.3%
-20.36191916 1
0.3%
-19.84035391 1
0.3%
-19.18658373 1
0.3%
-17.99213195 1
0.3%
-17.95005027 1
0.3%
-17.59617588 1
0.3%
-17.16419979 1
0.3%
-16.37834932 1
0.3%
-15.6414611 1
0.3%
ValueCountFrequency (%)
21.3868016 1
0.3%
19.16782776 1
0.3%
17.28829838 1
0.3%
17.08305769 1
0.3%
16.94885392 1
0.3%
16.15551813 1
0.3%
15.13449865 1
0.3%
15.05190433 1
0.3%
14.72988008 1
0.3%
13.79956569 1
0.3%
2023-06-17T10:07:21.565123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF
Distinct315
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8195157
Minimum-7.09338
Maximum16.975236
Zeros0
Zeros (%)0.0%
Memory size2.6 KiB
2023-06-17T10:07:21.699404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-7.09338
5-th percentile-4.0373463
Q11.2254962
median4.6240872
Q38.9256543
95-th percentile13.944128
Maximum16.975236
Range24.068616
Interquartile range (IQR)7.7001581

Descriptive statistics

Standard deviation5.5083852
Coefficient of variation (CV)1.1429334
Kurtosis-0.65624489
Mean4.8195157
Median Absolute Deviation (MAD)3.9922368
Skewness-0.038897298
Sum1518.1474
Variance30.342308
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.0009324379963
2023-06-17T10:07:21.780006image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.805478396 1
 
0.3%
-0.05648750269 1
 
0.3%
3.023427847 1
 
0.3%
2.401442416 1
 
0.3%
1.779456986 1
 
0.3%
1.54619297 1
 
0.3%
1.312928955 1
 
0.3%
1.07966494 1
 
0.3%
0.5115887185 1
 
0.3%
-0.6245637239 1
 
0.3%
Other values (305) 305
96.8%
ValueCountFrequency (%)
-7.093379952 1
0.3%
-7.052628707 1
0.3%
-6.775276006 1
0.3%
-6.680907877 1
0.3%
-6.459993439 1
0.3%
-6.268435802 1
0.3%
-6.144710871 1
0.3%
-5.855963728 1
0.3%
-5.829428304 1
0.3%
-5.724230681 1
0.3%
ValueCountFrequency (%)
16.97523605 1
0.3%
16.47851254 1
0.3%
16.29313537 1
0.3%
15.98178904 1
0.3%
15.61103469 1
0.3%
15.54329919 1
0.3%
15.48506554 1
0.3%
15.3540347 1
0.3%
15.1647702 1
0.3%
14.97550571 1
0.3%
2023-06-17T10:07:21.898844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF
Distinct315
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4258883
Minimum-65.246637
Maximum119.15888
Zeros0
Zeros (%)0.0%
Memory size2.6 KiB
2023-06-17T10:07:22.086914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-65.246637
5-th percentile-44.11175
Q1-11.330325
median7.3088081
Q323.99056
95-th percentile69.758575
Maximum119.15888
Range184.40552
Interquartile range (IQR)35.320885

Descriptive statistics

Standard deviation34.29585
Coefficient of variation (CV)3.638474
Kurtosis0.76050078
Mean9.4258883
Median Absolute Deviation (MAD)17.957356
Skewness0.58412631
Sum2969.1548
Variance1176.2053
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.001258584772
2023-06-17T10:07:22.746995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6.735186365 1
 
0.3%
19.81311968 1
 
0.3%
8.507489004 1
 
0.3%
8.696393936 1
 
0.3%
8.885298869 1
 
0.3%
13.47311671 1
 
0.3%
18.06093456 1
 
0.3%
22.6487524 1
 
0.3%
21.23093604 1
 
0.3%
18.39530333 1
 
0.3%
Other values (305) 305
96.8%
ValueCountFrequency (%)
-65.24663677 1
0.3%
-63.70609118 1
0.3%
-62.58798902 1
0.3%
-62.16554559 1
0.3%
-60.625 1
0.3%
-59.92934127 1
0.3%
-57.27069351 1
0.3%
-56.48148148 1
0.3%
-52.96368907 1
0.3%
-52.2940613 1
0.3%
ValueCountFrequency (%)
119.1588785 1
0.3%
115.0943396 1
0.3%
111.2716476 1
0.3%
108.561955 1
0.3%
104.6400075 1
0.3%
103.3844167 1
0.3%
102.0295704 1
0.3%
96.4062193 1
0.3%
95.49718574 1
0.3%
94.1856754 1
0.3%
2023-06-17T10:07:22.932387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF

Unemployment Rate (End Of Period) | Total Unemployment Rate
Numeric time series

HIGH CORRELATION  ZEROS 

Distinct273
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7922252
Minimum-38.636364
Maximum150
Zeros17
Zeros (%)5.4%
Memory size2.6 KiB
2023-06-17T10:07:23.096696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-38.636364
5-th percentile-31.081301
Q1-14.297462
median0
Q39.8611111
95-th percentile63.611111
Maximum150
Range188.63636
Interquartile range (IQR)24.158574

Descriptive statistics

Standard deviation30.584459
Coefficient of variation (CV)6.3820997
Kurtosis4.6119952
Mean4.7922252
Median Absolute Deviation (MAD)12.301587
Skewness1.8388588
Sum1509.5509
Variance935.40914
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.0002639313524
2023-06-17T10:07:23.198668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
 
5.4%
5.263157895 5
 
1.6%
5.555555556 5
 
1.6%
-20 3
 
1.0%
16.66666667 2
 
0.6%
9.161793372 2
 
0.6%
-14.28571429 2
 
0.6%
50 2
 
0.6%
-5.263157895 2
 
0.6%
-3.921568627 2
 
0.6%
Other values (263) 273
86.7%
ValueCountFrequency (%)
-38.63636364 1
0.3%
-38.46153846 1
0.3%
-38.14102564 1
0.3%
-37.93103448 1
0.3%
-37.82051282 1
0.3%
-37.5 1
0.3%
-36.58536585 1
0.3%
-35.90250329 1
0.3%
-35.85646201 1
0.3%
-34.97435897 1
0.3%
ValueCountFrequency (%)
150 1
0.3%
141.6666667 1
0.3%
133.3333333 1
0.3%
125 1
0.3%
111.9047619 1
0.3%
110.5263158 1
0.3%
98.80952381 1
0.3%
87.5 1
0.3%
85.71428571 1
0.3%
79.16666667 1
0.3%
2023-06-17T10:07:23.373045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF

Interactions

2023-06-17T10:07:16.663001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-17T10:07:06.278415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-17T10:07:07.183666image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-17T10:07:08.050918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-17T10:07:08.774190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-17T10:07:09.490187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-17T10:07:10.320710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-17T10:07:10.991355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-17T10:07:11.659340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-17T10:07:12.476769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-17T10:07:13.244296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-17T10:07:13.905013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-17T10:07:14.614908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-17T10:07:15.306651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-17T10:07:15.981030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-17T10:07:16.708731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-17T10:07:06.400668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-17T10:07:07.249120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-17T10:07:08.103395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-06-17T10:07:10.944836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-17T10:07:11.619578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-17T10:07:12.428495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-17T10:07:13.202463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-17T10:07:13.856980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-17T10:07:14.563998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-17T10:07:15.264005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-17T10:07:15.935666image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-17T10:07:16.617277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-06-17T10:07:23.534093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Gross Domestic Product, Year On Year Growth Rate | GDP In Chained (2015) DollarsFood & Beverage Services Index, (2017 = 100), In Chained Volume Terms | TotalRetail Sales Index, (2017 = 100), In Chained Volume Terms | TotalSea Cargo And Shipping Statistics (Total Cargo) | Total Container Throughput (Thousand Twenty-Foot Equivalent Units)Air Cargo Tonnage | Total Direct Tonnage (Tonne)Merchandise Imports By Commodity Division | Total Merchandise ImportsM1 Money SupplyIndex Of Industrial Production (2019 = 100) | TotalDomestic Exports Of Major Non-Oil Products | Total Electronic ProductsComposite Leading Index (2015 = 100) | Quarterly Composite Leading IndexBusiness Expectations For The Services Sector - General Business Outlook For The Next 6 Months, Weighted Percentages Of Up, Same, Down | Net Weighted Balance - Total Services SectorDomestic Wholesale Trade Index, (2017 = 100), In Chained Volume Terms | TotalForeign Wholesale Trade Index, (2017 = 100), In Chained Volume Terms | TotalJob Vacancies By Industry And Occupational Group (SSIC 2020) (End Of Period) | TotalUnemployment Rate (End Of Period) | Total Unemployment Rate
Gross Domestic Product, Year On Year Growth Rate | GDP In Chained (2015) Dollars1.0000.1310.4370.4930.2780.6960.3030.5640.4850.6840.7670.4620.6780.725-0.635
Food & Beverage Services Index, (2017 = 100), In Chained Volume Terms | Total0.1311.0000.0070.027-0.118-0.128-0.108-0.134-0.293-0.059-0.1160.1340.101-0.094-0.156
Retail Sales Index, (2017 = 100), In Chained Volume Terms | Total0.4370.0071.0000.2160.1500.4490.0400.1900.3170.2790.4690.2320.3410.365-0.324
Sea Cargo And Shipping Statistics (Total Cargo) | Total Container Throughput (Thousand Twenty-Foot Equivalent Units)0.4930.0270.2161.0000.1210.4680.0790.3480.2700.5540.4300.2770.2800.321-0.166
Air Cargo Tonnage | Total Direct Tonnage (Tonne)0.278-0.1180.1500.1211.0000.4030.0750.2650.1660.2920.326-0.0020.0990.476-0.315
Merchandise Imports By Commodity Division | Total Merchandise Imports0.696-0.1280.4490.4680.4031.0000.2850.5550.6000.5290.6020.1900.4510.673-0.497
M1 Money Supply0.303-0.1080.0400.0790.0750.2851.0000.2460.1190.3440.340-0.1060.2170.403-0.215
Index Of Industrial Production (2019 = 100) | Total0.564-0.1340.1900.3480.2650.5550.2461.0000.5350.5650.5030.1840.2710.484-0.191
Domestic Exports Of Major Non-Oil Products | Total Electronic Products0.485-0.2930.3170.2700.1660.6000.1190.5351.0000.4920.4340.1570.4400.410-0.223
Composite Leading Index (2015 = 100) | Quarterly Composite Leading Index0.684-0.0590.2790.5540.2920.5290.3440.5650.4921.0000.7140.2220.4020.643-0.258
Business Expectations For The Services Sector - General Business Outlook For The Next 6 Months, Weighted Percentages Of Up, Same, Down | Net Weighted Balance - Total Services Sector0.767-0.1160.4690.4300.3260.6020.3400.5030.4340.7141.0000.3280.5190.761-0.522
Domestic Wholesale Trade Index, (2017 = 100), In Chained Volume Terms | Total0.4620.1340.2320.277-0.0020.190-0.1060.1840.1570.2220.3281.0000.4650.259-0.318
Foreign Wholesale Trade Index, (2017 = 100), In Chained Volume Terms | Total0.6780.1010.3410.2800.0990.4510.2170.2710.4400.4020.5190.4651.0000.419-0.542
Job Vacancies By Industry And Occupational Group (SSIC 2020) (End Of Period) | Total0.725-0.0940.3650.3210.4760.6730.4030.4840.4100.6430.7610.2590.4191.000-0.593
Unemployment Rate (End Of Period) | Total Unemployment Rate-0.635-0.156-0.324-0.166-0.315-0.497-0.215-0.191-0.223-0.258-0.522-0.318-0.542-0.5931.000

Missing values

2023-06-17T10:07:17.377960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-17T10:07:17.671400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PeriodGross Domestic Product, Year On Year Growth Rate | GDP In Chained (2015) DollarsFood & Beverage Services Index, (2017 = 100), In Chained Volume Terms | TotalRetail Sales Index, (2017 = 100), In Chained Volume Terms | TotalSea Cargo And Shipping Statistics (Total Cargo) | Total Container Throughput (Thousand Twenty-Foot Equivalent Units)Air Cargo Tonnage | Total Direct Tonnage (Tonne)Merchandise Imports By Commodity Division | Total Merchandise ImportsM1 Money SupplyIndex Of Industrial Production (2019 = 100) | TotalDomestic Exports Of Major Non-Oil Products | Total Electronic ProductsComposite Leading Index (2015 = 100) | Quarterly Composite Leading IndexBusiness Expectations For The Services Sector - General Business Outlook For The Next 6 Months, Weighted Percentages Of Up, Same, Down | Net Weighted Balance - Total Services SectorDomestic Wholesale Trade Index, (2017 = 100), In Chained Volume Terms | TotalForeign Wholesale Trade Index, (2017 = 100), In Chained Volume Terms | TotalJob Vacancies By Industry And Occupational Group (SSIC 2020) (End Of Period) | TotalUnemployment Rate (End Of Period) | Total Unemployment Rate
01997-01-315.766667111.14715.7150985.48821293891.41.6004489.938033-5.407013-0.6365982.0390525.333333-4.9532534.805478-6.735186-14.074074
11997-02-285.733333109.571-19.7114700.51126995647.4-11.0221866.894096-3.535354-5.9003912.3935723.666667-1.0139236.974227-5.116564-17.037037
21997-03-315.700000118.4473.65899316.21145494048.81.8933309.790800-6.288319-5.0654842.7480922.0000002.9254079.142976-3.497942-20.000000
31997-04-306.966667107.9742.9710009.48126894470.62.1791684.4346632.294903-1.7444943.1471858.6666676.86023210.549475-3.759513-20.277778
41997-05-318.233333113.7952.0816627.93708994257.86.8452825.6323410.208366-7.1575383.54627815.33333310.79505611.955975-4.021084-20.555556
51997-06-309.500000115.3475.42013810.24494791569.44.9257526.92417811.063939-3.5434683.94537222.00000014.72988013.362475-4.282655-20.833333
61997-07-319.966667110.31210.3363139.32773891285.811.1589766.8610768.504600-1.0347233.95138620.00000016.94885413.054822-2.629370-16.919192
71997-08-3110.433333110.8454.1848839.49047491135.615.0377007.1964716.958866-3.2252833.95740018.00000019.16782812.747169-0.976084-13.005051
81997-09-3010.900000115.0284.10691911.18172693635.017.9539425.51874614.3961871.7368783.96341516.00000021.38680212.4395160.677201-9.090909
91997-10-319.633333112.639-3.52491911.80451890553.27.8849496.2629627.750873-2.0817582.8430803.33333316.1555189.806800-4.284904-6.060606
PeriodGross Domestic Product, Year On Year Growth Rate | GDP In Chained (2015) DollarsFood & Beverage Services Index, (2017 = 100), In Chained Volume Terms | TotalRetail Sales Index, (2017 = 100), In Chained Volume Terms | TotalSea Cargo And Shipping Statistics (Total Cargo) | Total Container Throughput (Thousand Twenty-Foot Equivalent Units)Air Cargo Tonnage | Total Direct Tonnage (Tonne)Merchandise Imports By Commodity Division | Total Merchandise ImportsM1 Money SupplyIndex Of Industrial Production (2019 = 100) | TotalDomestic Exports Of Major Non-Oil Products | Total Electronic ProductsComposite Leading Index (2015 = 100) | Quarterly Composite Leading IndexBusiness Expectations For The Services Sector - General Business Outlook For The Next 6 Months, Weighted Percentages Of Up, Same, Down | Net Weighted Balance - Total Services SectorDomestic Wholesale Trade Index, (2017 = 100), In Chained Volume Terms | TotalForeign Wholesale Trade Index, (2017 = 100), In Chained Volume Terms | TotalJob Vacancies By Industry And Occupational Group (SSIC 2020) (End Of Period) | TotalUnemployment Rate (End Of Period) | Total Unemployment Rate
3052022-06-304.50000091.57110.0105841.659978109517.032.43642813.8734423.2930544.0638390.35842315.000000-17.9500500.93873235.394456-23.529412
3062022-07-314.33333396.7259.6062025.208734109693.032.36239813.0566701.78252410.321457-0.08990013.000000-13.9973551.68785824.555997-22.352941
3072022-08-314.16666795.1148.8651732.513905101835.030.7540527.1554580.990843-4.549278-0.53822411.000000-10.0446602.43698313.717538-21.176471
3082022-09-304.00000093.0087.764216-1.719382101207.021.3546286.9678681.144870-10.607850-0.9865479.000000-6.0919653.1861092.879079-20.000000
3092022-10-313.36666796.4237.5637522.921107101512.010.9244075.816043-0.644118-9.267667-2.4651057.000000-4.3651322.531959-1.384708-18.095238
3102022-11-302.73333394.6723.528764-6.17460399219.0-0.0414433.938170-3.701242-20.219001-3.9436645.000000-2.6383001.877809-5.648494-16.190476
3112022-12-312.100000102.9944.257838-1.19373199029.0-8.2422086.151695-3.505964-17.856474-5.4222223.000000-0.9114671.223659-9.912281-14.285714
3122023-01-311.53333399.578-4.899654-4.90504885680.0-11.2275876.068394-3.013121-26.816758-5.4617023.3333332.8344110.420000-5.828636-16.190476
3132023-02-280.96666788.6978.642604-5.59521787288.0-4.7103065.942270-9.611680-26.537747-5.5011823.6666676.580290-0.383658-5.775872-18.095238
3142023-03-310.40000094.4302.4853967.96245397640.0-10.9548116.632419-3.782552-22.266391-5.5406614.00000010.326168-1.187316-4.783451-20.000000